Meet our interdisciplinary team of researchers, PhD students, and collaborators.
Professor Mihaela van der Schaar
Mihaela van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Turing Faculty Fellow at The Alan Turing Institute in London, a Chancellor’s Professor at UCLA and an IEEE Fellow.
Professor Andres Floto
Andres Floto is a Professor of Respiratory Biology at the University of Cambridge, a Wellcome Trust Senior Investigator, and Director of the UK Cystic Fibrosis Innovation Hub.
Dr Sarah Teichmann FMedSci FRS
Head of Cellular Genetics and Senior Group Leader, Wellcome Sanger Institute
Dr Teichmann is a world-leading scientist who combines her expertise in computational and systems biology with single-cell biology, genomics and immunology. She applies her knowledge using novel approaches to answer questions fundamental to our understanding of biology and medicine.
Dr Ari Ercole
Consultant and Researcher in anaesthesia and intensive care • Fellow in Clinical Medicine at Magdalene College, University of Cambridge
Dr Ercole is a consultant in anaesthesia and intensive care medicine. He also holds a PhD in experimental physics from the University of Cambridge. He divides his time between clinical practice and research.
Professor Stefan Scholtes
Dennis Gillings Professor of Health Management, Cambridge Judge Business School • Director of the Centre for Health Leadership & Enterprise
Professor Scholtes‘s research is strongly practice-based and embedded in close collaborations with the Cambridge University Hospitals NHS Foundation Trust, Cambridgeshire and Peterborough Foundation Trust, and Public Health England.
Dr Eoin McKinney
University Lecturer in Renal medicine at the University of Cambridge • Honorary consultant in nephrology and transplantation, Cambridge University Hospitals NHS Foundation Trust
Dr McKinney’s research explores the interface between immune responses to infection and those driving inflammatory pathology, applying machine learning methods to the integration of multi-omics data, building interpretable predictive models for rapid translation into clinical practice while informing underlying disease biology and identifying novel therapeutic strategies.
Dr Alexander Gimson
Consultant Transplant Hepatologist, Cambridge University Hospitals NHS Foundation Trust • Chair of the Care Advisory Group, Cambridgeshire & Peterborough Sustainability and Transformation Partnership
Dr Gimson led the national team which developed in a new organ allocation offering scheme whereby organs are offered to the person on a national waiting list who has the greatest calculated net life years gained from the particular donor organ.
He is running a project which aims to discover if an AI/machine learning model can beat existing models, to make that organ-offering even more equitable.
Dr Angela Wood
Reader in Health Data Science at the University of Cambridge
Dr Wood‘s research interests are centred on the development and application of statistical methods for advancing epidemiological research. She has focused on developing statistical methodology for handling measurement error, using repeated measures of risk factors, missing data problems, multiple imputation, risk prediction and meta-analysis.
Professor Pietro Liò
Professor of Computational Biology in the Department of Computer Science at the University of Cambridge • Member of the Artificial Intelligence group of the Computer Laboratory
Professor Liò has PhDs in Complex Systems and Non Linear Dynamics, and in Theoretical Genetics. He is the author of over 400 papers. His specialties include bioinformatics algorithms, predictive models in personalised medicine, modeling comorbidity and aging, methods for combining multi-scale biological processes, statistics of multi omics and multi physics modelling of molecules-cell-tissue-organ interactions.
Dr José Miguel Hernández-Lobato
University Lecturer (US equivalent to Assistant Professor) in Machine Learning at the University of Cambridge
Dr Hernández-Lobato‘s research revolves around model-based machine learning with a focus on probabilistic learning techniques and with a particular interest on Bayesian optimisation, matrix factorization methods, copulas, Gaussian processes and sparse linear models.